Compiling the Lexicographic Inference Using Boolean Cardinality Constraints

  • Authors:

  • Safa Yahi;Salem Benferhat

  • Affiliations:

  • Université Lille-Nord de France, Artois, F-62307 Lens, CRIL, F-62307 Lens, CNRS UMR 8188, F-62307 Lens,;Université Lille-Nord de France, Artois, F-62307 Lens, CRIL, F-62307 Lens, CNRS UMR 8188, F-62307 Lens,

  • Venue:
  • Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper sheds light on the lexicographic inference from stratified belief bases which is known to have desirable properties from theoretical, practical and psychological points of view. However, this inference is expensive from the computational complexity side. Indeed, it amounts to a $\Delta_2^p$-complete problem. In order to tackle this hardness, we propose in this work a new compilation of the lexicographic inference using the so-called Boolean cardinality constraints. This compilation enables a polynomial time lexicographic inference and offers the possibility to update the priority relation between the strata without any re-compilation. Moreover, it can be efficiently extended to deal with the lexicographical closure inference which takes an important place in default reasoning. Furthermore, unlike the existing compilation approaches of the lexicographic inference, ours can be efficiently parametrized by any target compilation language. In particular, it enables to take advantage of the well-known prime implicates language which has been quite influential in artificial intelligence and computer science in general.